Asymptotically Optimal Distributed Filtering of Continuous-Time Linear Systems

被引：0

作者：

Battilotti, S. ^{[1
]}

Cacace, F. ^{[2
,3
]}

d'Angelo, M. ^{[1
]}

Germani, A. ^{[4
]}

机构：

[1] Univ Sapienza Roma, DIAG, Rome, Italy

[2] Univ Campus Biomed Roma, Rome, Italy

[3] CNR, IASI, Biomath Lab, Rome, Italy

[4] Univ Aquila, DISIM, Laquila, Italy

来源：

IFAC PAPERSONLINE | 2020年 / 53卷 / 02期

关键词：

Continuous time filters; Kalman filters; Filtering theory; Consensus filters; KALMAN CONSENSUS;

D O I：

10.1016/j.ifacol.2020.12.1124

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we prove the following new and unexpected result: it is possible to design a continuous-time distributed filter for linear systems that asymptotically tends at each node to the optimal centralized filter. The result concerns distributed estimation over a connected undirected graph and it only requires to exchange the estimates among adjacent nodes. We exhibit an algorithm containing a consensus term with a parametrized gain and show that when the parameter becomes arbitrarily large the error covariance at each node becomes arbitrarily close to the error covariance of the optimal centralized Kalman filter. Copyright (C) 2020 The Authors.

引用

页码：3242 / 3247

页数：6

共 13 条

[1] Consensus-Based Linear and Nonlinear Filtering [J].